QIAL@ SPIE Medical Imaging 2024

QIAL Contributions at SPIE Medical Imaging, San Diego, 2024

Predicting brain age and associated structural networks in mouse models with humanized APOE alleles using integrative and interpretable graph neural networks
HS Moon, A Mahzarnia, J Stout, RJ Anderson, ZY Han, CT Badea, …
Medical Imaging 2024: Computer-Aided Diagnosis 12927, 268-275 2024
Volumetric brain region segmentation and morphometry in mouse models using high-resolution hybrid micro-CT imaging
R Nadkarni, ZY Han, RJ Anderson, A Allphin, DP Clark, A Badea, …
Medical Imaging 2024: Clinical and Biomedical Imaging 12930, 162-171 2024
Whole heart CNN-based segmentation for phenotypical analysis of APOE mouse models using photon counting cine cardiac micro-CT data
AJ Allphin, R Nadkarni, Z Han, DP Clark, A Badea, CT Badea
Medical Imaging 2024: Clinical and Biomedical Imaging 12930, 100-104 2024
Photon counting micro-CT imaging of Bi2WO6 nanoparticles
CT Badea, R Bhavane, A Allphin, R Nadkarni, DP Clark, AV Annapragada, …
Medical Imaging 2024: Clinical and Biomedical Imaging 12930, 311-319 2024
Enhancing in vivo preclinical studies with VivoVist™ and photon-counting micro-CT imaging
CT Badea, A Rickard, A Allphin, DP Clark, KB Ghaghada, S Ridwan, …
Medical Imaging 2024: Clinical and Biomedical Imaging 12930, 236-242 2024
Deep learning models for rapid denoising of 5D cardiac photon-counting micro-CT images
R Nadkarni, DP Clark, A Allphin, CT Badea
Medical Imaging 2024: Physics of Medical Imaging 12925, 547-556 2024
Multi-channel reconstruction (MCR) toolkit v2. 0: open-source Python-based tools for x-ray CT reconstruction
DP Clark, A Allphin, R Nadkarni, CT Badea
Medical Imaging 2024: Physics of Medical Imaging 12925, 454-459 2024
Denoising pediatric cardiac photon-counting CT data using volumetric vision transformers and unpaired training data
DP Clark, FR Schwartz, JY Cao, CT Badea
Medical Imaging 2024: Physics of Medical Imaging 12925, 254-262 2024
Advancing preclinical micro-photon counting CT perfusion imaging: from phantom experiments to in vivo applications
AJ Allphin, DP Clark, CJ Gil, ML Tomov, V Serpooshan, CT Badea
Medical Imaging 2024: Physics of Medical Imaging 12925, 72-79 2024
An acquisition-based approach for high-fidelity infilling of photon-counting x-ray detector pixel gaps
AJ Allphin, R Nadkarni, DP Clark, CT Badea
Medical Imaging 2024: Physics of Medical Imaging 12925, 536-541, 2024

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Congratulations to Hae Sol Moon!

Congratulations to  runner-up Hae Sol Moon of the Robert F. Wagner All-Conference Best Student Paper Award, sponsored by SPIE and the Medical Imaging Perception Society!   

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Gene Set Analysis Links Vascular Endothelial Growth Factor to Alzheimer’s via ITGA5

Our new study delves into Alzheimer’s disease, revealing molecular pathways tied to hallmark biomarkers & cognitive decline. Pathway analysis identified VEGF-RB as a common link, offering insights into disease mechanisms & potential treatment avenues.

Mahzarnia, Ali, Lutz, Michael W., and Badea, Alexandra. ‘A Continuous Extension of Gene Set Enrichment Analysis Using the Likelihood Ratio Test Statistics Identifies Vascular Endothelial Growth Factor as a Candidate Pathway for Alzheimer’s Disease via ITGA5’. 1 Jan. 2024 : 635 – 648. Journal of Alzheimer’s Disease, vol. 97, no. 2, pp. 635-648, 2024.DOI: 10.3233/JAD-230934

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Impact of Age Related Macular Degeneration through Advanced Imaging

Exciting research on  Age-related Macular Degeneration (AMD)  and its impact on the brain! Using diffusion MRI, we observed significant brain changes in AMD patients, including reduced volume in key cognitive areas and altered brain connections.

Stout, J.A.; Mahzarnia, A.; Dai, R.; Anderson, R.J.; Cousins, S.; Zhuang, J.; Lad, E.M.; Whitaker, D.B.; Madden, D.J.; Potter, G.G.; et al. Accelerated Brain Atrophy, Microstructural Decline and Connectopathy in Age-Related Macular DegenerationBiomedicines 202412, 147. https://doi.org/10.3390/biomedicines12010147 


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Understanding Alzheimer’s, brain aging, & the role of APOE2 allele

In our study, we adopted an integrative approach that combined behavioral assessments, brain imaging, and blood transcriptomics to focus on the transition from middle to old age in APOE2 mice, leading to the identification of critical brain sub-networks and regions such as the cingulate cortex; we utilized Sparse Multiple Canonical Correlation Analysis (SMCCA) for modeling and graph neural network predictions, providing valuable insights into brain connectivity, aging, sex differences, and potential therapeutic targets.

Hae Sol Moon, Ali Mahzarnia, Jacques Stout, Robert J. Anderson, Madison Strain, Jessica T. Tremblay, Zay Yar Han, Andrei Niculescu, Anna MacFarlane, Jasmine King, Allison Ashley-Koch, Darin Clark, Michael W. Lutz & Alexandra Badea, Multivariate Investigation of Aging in Mouse Models with Alzheimer’s Protective APOE2 Allele: A Fusion of Cognitive Metrics, Brain Imaging, & Blood Transcriptomics, Brain Structure and Function, https://doi.org/10.1007/s00429-023-02731-x 


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A photon counting CT imaging pipeline for cardiac phenotyping in APOE mice

Our new study develops an advanced imaging pipeline using photon-counting CT to assess cardiac structure & function in mouse models of apolipoprotein E gene variations. Results highlight genotype-specific cardiovascular differences and the impact of diet.

Allphin, A. J., Mahzarnia, A., Clark, D. P., Qi, Y., Han, Z. Y., Bhandari, P., Ghaghada, K. B., Badea, A., & Badea, C. T. (2023). Advanced photon counting CT imaging pipeline for cardiac phenotyping of apolipoprotein E mouse models. PLoS ONE 18(10): e0291733, https://doi.org/10.1371/journal.pone.0291733.


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Leveraging 3D Bioprinting & Photon-Counting Computed Tomography for Regenerative Medicine

An exciting publication on our collaborative research focusing on the innovative applications of 3D Bioprinting and Photon-Counting CT.

Gil CJ, Evans CJ, Li L, Allphin AJ, Tomov ML, Jin L, Vargas M, Hwang B, Wang J, Putaturo V, Kabboul G, Alam AS, Nandwani RK, Wu Y, Sushmit A, Fulton T, Shen M, Kaiser JM, Ning L, Veneziano R, Willet N, Wang G, Drissi H, Weeks ER, Bauser-Heaton HD, Badea CT, Roeder RK, Serpooshan V. Leveraging 3d Bioprinting And Photon-Counting Computed Tomography to Enable Noninvasive Quantitative Tracking of Multifunctional Tissue Engineered Constructs. Adv Healthc Mater. 2023 Sep 14:e2302271. doi: 10.1002/adhm.202302271. PMID: 37709282.

Read the full paper here

A big thank you to our team and collaborators..

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Deep Learning Denoising for Photon-Counting CT

Nadkarni, R.; Clark, D.P.; Allphin, A.J.; Badea, C.T. A Deep Learning Approach for Rapid and Generalizable Denoising of Photon-Counting Micro-CT Images. Tomography 20239, 1286-1302. https://doi.org/10.3390/tomography9040102

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MCR toolkit

Our QIAL developed Multi-Channel CT Reconstruction (MCR) Toolkit addresses unique challenges associated with in vivo preclinical micro-CT and supporting the translation of these advancements to the clinical domain.

We have now published a paper in Medical Physics detailing our toolkit’s capabilities and functions:

Clark DP, Badea CT. MCR toolkit: A GPU-based toolkit for multi-channel reconstruction of preclinical and clinical x-ray CT data. Med. Phys.. 2023;1-22. https://doi.org/10.1002/mp.16532 

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Co-Clinical Imaging

Our group contributed to these new papers from the co-clinical imaging research program (CIRP) :

1.Moore, S.M.; Quirk, J.D.; Lassiter, A.W.; Laforest, R.; Ayers, G.D.; Badea, C.T.; Fedorov, A.Y.; Kinahan, P.E.; Holbrook, M.; Larson, P.E.Z.; Sriram, R.; Chenevert, T.L.; Malyarenko, D.; Kurhanewicz, J.; Houghton, A.M.; Ross, B.D.; Pickup, S.; Gee, J.C.; Zhou, R.; Gammon, S.T.; Manning, H.C.; Roudi, R.; Daldrup-Link, H.E.; Lewis, M.T.; Rubin, D.L.; Yankeelov, T.E.; Shoghi, K.I. Co-Clinical Imaging Metadata Information (CIMI) for Cancer Research to Promote Open Science, Standardization, and Reproducibility in Preclinical ImagingTomography 20239, 995-1009. https://doi.org/10.3390/tomography9030081

2.Gammon, S.T.; Cohen, A.S.; Lehnert, A.L.; Sullivan, D.C.; Malyarenko, D.; Manning, H.C.; Hormuth, D.A.; Daldrup-Link, H.E.; An, H.; Quirk, J.D.; Shoghi, K.; Pagel, M.D.; Kinahan, P.E.; Miyaoka, R.S.; Houghton, A.M.; Lewis, M.T.; Larson, P.; Sriram, R.; Blocker, S.J.; Pickup, S.; Badea, A.; Badea, C.T.; Yankeelov, T.E.; Chenevert, T.L. An Online Repository for Pre-Clinical Imaging Protocols (PIPs)Tomography 20239, 750-758. https://doi.org/10.3390/tomography9020060


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